• Developed, optimized, and monitored ADE pipelines, utilizing advanced SQL queries in Snowflake to deliver accurate regulatory reporting, reducing reporting errors by 25%.• Engineered and deployed ETL pipelines using Azure Data Factory (ADF) for seamless data migration and integration across systems.• Managed EC2 instances for running data processing workloads, optimizing compute capacity usage by 20%, and reducing operational costs by 15%.• Utilized Agile and Scrum frameworks, achieving a 20% increase in project delivery efficiency through improved sprint management and collaboration.• Architected and maintained data lakes on AWS S3, enabling efficient data storage, retrieval, and analysis for machine learning workloads.• Designed interactive dashboards in Tableau, improving decision-making effectiveness by 35% through enhanced data visualization.• Automated data pipeline processes using Git, Terraform, and Jenkins, reducing manual intervention and human error by 50%.• Optimized SQL queries, resulting in a 35% improvement in query execution times and overall system performance.• Utilized Kubernetes and Docker, creating a scalable runtime environment for CI/CD pipelines, reducing deployment times by 50%.

• Engineered real-time data pipelines using PySpark and Spark Structured Streaming in Databricks, processing 70GB daily, which slashed data latency by 80% and enhanced decision-making.• Executed Change Data Capture strategies with Delta Lake and AWS S3, ensuring 99.9% data accuracy for more reliable inventory and sales forecasting.• Constructed a customer analytics data mart in Amazon Redshift, boosting marketing targeting accuracy by 30%, thereby tailoring promotions and offers more effectively.• Spearheaded real-time analytics workflows with Spark Streaming in Databricks, reducing data latency to under 2 seconds, thereby accelerating operational decision-making.• Deployed Apache Airflow to orchestrate and automate complex ETL workflows, streamlining processes across PySpark, Delta Lake, and AWS services, which increased process efficiency and cut manual oversight by 50%.• Optimized cloud costs by 25% by implementing auto-scaling and utilizing spot instances in Databricks, ensuring high performance and availability for large-scale retail data operations.

•Conducted extensive data mining on large-scale relational databases like Oracle, accessing, creating, and maintaining detailed and summary tables, which improved query performance by 30% and enabled more efficient retrieval of patient and operational data.•Implemented real-time data streaming using Apache Kafka, facilitating low-latency data ingestion and processing that reduced data lag by 25%, thereby enhancing the speed of critical healthcare decision-making.•Designed and optimized a data warehouse using Snowflake to support complex healthcare analytics tasks, resulting in a 40% increase in query performance and optimized data storage for reporting on patient outcomes and operational metrics.•Created interactive dashboards and reports in Tableau and Power BI that provided healthcare stakeholders with easy access to actionable insights, leading to a 35% improvement in data-driven decision-making across clinical and operational teams.•Conducted performance tuning and optimization of SQL queries and Spark jobs, reducing data processing run times by 45% to ensure efficient resource utilization and timely access to healthcare data for reporting and analysis.•Utilized Excel for advanced data analysis, leveraging pivot tables, charts, and complex functions to deliver insights that enhanced operational efficiency by 15%, particularly in areas related to inventory management and patient care.•Performed data retrieval using Confidential Data Studio and PostgreSQL, adhering to strict data integrity and security protocols, which ensured 100% compliance with healthcare data privacy regulations such as HIPAA.